부산대학교 도서관

Natural language processing models based on machine learning (ML‐NLP models) have been developed to solve practical problems, such as interpreting an Internet search query. These models are not intended to reflect human language comprehension mechanisms, and the word representations used by ML‐NLP models and human brains might therefore be quite different. However, because ML‐NLP models are trained with the same kinds of inputs that humans must process, and they must solve many of the same computational problems as the human brain, ML‐NLP models and human brains may end up with similar word representations. To distinguish between these hypotheses, we used representational similarity analysis to compare the representational geometry of word representations in two ML‐NLP models with the representational geometry of the human brain, as indexed with event‐related potentials (ERPs). Participants listened to stories while the electroencephalogram was recorded. We extracted averaged ERPs for each of the 100 words that occurred most frequently in the stories, and we calculated the similarity of the neural response for each pair of words. We compared this 100 × 100 similarity matrix to the 100 × 100 similarity matrix for the word pairs according to two ML‐NLP models. We found significant representational similarity between the neural data and each ML‐NLP model, beginning within 250 ms of word onset. These results indicate that ML‐NLP systems that are designed to solve practical technology problems have a representational geometry that is correlated with that of the human brain, presumably because both are influenced by the structural properties and statistics of language. This study asked whether natural language processing models that are designed to solve practical problems end up with word representations that are similar to those of the human brain. We recorded EEG activity for words presented in natural stories, and we found that the word‐to‐word similarity structure of the brain activity was correlated with the similarity structure of two models. Thus, the human brain and natural language processing models have similar patterns of word representations. [ABSTRACT FROM AUTHOR]